Useful R functions (preview)

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1] 374 299 106 244 295 789 797 148 470  82 695 836 800 530 929 441 724  72 830 483

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1] 441 800 470 530 483  NA 929 106 836 830 695  NA  NA 797 244 299  82 789 148 374 724  72 295

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 1 2 4 5 2 4 3 5 2 4

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "m" "s" "c" "p" "w" "T" "Y" "N" "X" "Z"

Are all/any elements TRUE

Input: logical vector
Output: single logical value
Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1] 15

which( manyNumbersWithNA > 900 )

[1] 7

which( is.na( manyNumbersWithNA ) )

[1]  6 12 13

Filtering vector elements

Input: any vector and filtering condition
Output: elements of the input vector
Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 929

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 929

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 929

Are some elements among other elements

Input: two vectors
Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "T" "Y" "N" "X" "Z"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "m" "s" "c" "p" "w"

manyNumbers %in% 300:600

 [1]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE

which( manyNumbers %in% 300:600 )

[1]  1  9 14 16 20

sum( manyNumbers %in% 300:600 )

[1] 5

Pick one of two (three) depending on condition

Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
Output: elements of the additional vectors
Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "small" "large" "small" "large" "small" NA      "large" "small" "large" "large" "large" NA      NA      "large" "small"
[16] "small" "small" "large" "small" "small" "large" "small" "small"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "small"   "large"   "small"   "large"   "small"   "UNKNOWN" "large"   "small"   "large"   "large"   "large"   "UNKNOWN"
[13] "UNKNOWN" "large"   "small"   "small"   "small"   "large"   "small"   "small"   "large"   "small"   "small"

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L )

 [1]   0 800   0 530   0  NA 929   0 836 830 695  NA  NA 797   0   0   0 789   0   0 724   0   0

Duplicates and unique elements

Input: a vector

unique( duplicatedNumbers )

[1] 1 2 4 5 3

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  1  2  4  5  3

duplicated( duplicatedNumbers )

 [1] FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 7

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 929

which.min( manyNumbersWithNA )

[1] 22

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 72

range( manyNumbersWithNA, na.rm = TRUE )

[1]  72 929

Sorting/ordering of vectors

manyNumbersWithNA

 [1] 441 800 470 530 483  NA 929 106 836 830 695  NA  NA 797 244 299  82 789 148 374 724  72 295

sort( manyNumbersWithNA )

 [1]  72  82 106 148 244 295 299 374 441 470 483 530 695 724 789 797 800 830 836 929

sort( manyNumbersWithNA, na.last = TRUE )

 [1]  72  82 106 148 244 295 299 374 441 470 483 530 695 724 789 797 800 830 836 929  NA  NA  NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 929 836 830 800 797 789 724 695 530 483 470 441 374 299 295 244 148 106  82  72  NA  NA  NA

manyNumbersWithNA[1:5]

[1] 441 800 470 530 483

order( manyNumbersWithNA[1:5] )

[1] 1 3 5 4 2

rank( manyNumbersWithNA[1:5] )

[1] 1 5 2 4 3

sort( mixedLetters )

 [1] "c" "m" "N" "p" "s" "T" "w" "X" "Y" "Z"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 2.5 1.0 7.5 2.5 9.5 5.0 7.5 9.5 5.0 5.0

rank( manyDuplicates, ties.method = "min" )

 [1] 2 1 7 2 9 4 7 9 4 4

rank( manyDuplicates, ties.method = "random" )

 [1]  2  1  7  3 10  5  8  9  6  4

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.0000000 -0.5000000  0.0000000  0.5000000  1.0000000  0.5737483  0.9786603  0.6564224 -0.9615221  0.3433038 -0.3763285
[12] -0.3827227 -0.9142012  0.1349938  0.2617948

round( v, 0 )

 [1] -1  0  0  0  1  1  1  1 -1  0  0  0 -1  0  0

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0  0.6  1.0  0.7 -1.0  0.3 -0.4 -0.4 -0.9  0.1  0.3

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00  0.57  0.98  0.66 -0.96  0.34 -0.38 -0.38 -0.91  0.13  0.26

floor( v )

 [1] -1 -1  0  0  1  0  0  0 -1  0 -1 -1 -1  0  0

ceiling( v )

 [1] -1  0  0  1  1  1  1  1  0  1  0  0  0  1  1

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 × 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"

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